An Intelligent System for Fresh Bitter Gourd Detection Using CNN
| dc.contributor.author | Tasnim, Zarin | |
| dc.date.accessioned | 2021-12-08T08:45:11Z | |
| dc.date.available | 2021-12-08T08:45:11Z | |
| dc.date.issued | 2021-09-20 | |
| dc.description.abstract | Agriculture development is not only a normal development sector but also a vital sector all over the world. Convolution Neural Network is one of the most advanced algorithms in Machine Learning. In my study, I have built up a strong relationship between agriculture and Image processing system, bitter gourd freshness detection and automation system using multi-layer automation process. I have used 5*3 training layers for the dataset and relevant output process. In this study, I show 4 types of output like Fresh, Moderate, Wrong and rotten bitter gourd. After analyzing data and method implementation I get 91.56% model accuracy which is better than the other image processing algorithm. In the modern era agriculture development is the highly contribute field of food security. This study will allow farmers to choose the proper crop in the right market condition, which will play a key role in strengthening the economy of the country. Technology on the other hand is a huge blessing in people's lives. In today's world, the introduction of information technology in agriculture has led to great improvements in this field. | |
| dc.identifier.other | http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6539 | |
| dc.identifier.uri | http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6539 | |
| dc.language.iso | en_US | |
| dc.publisher | Daffodil International University | |
| dc.source | DIU Institutional Repository | |
| dc.subject | Sustainable agriculture | |
| dc.subject | Technology assessment | |
| dc.title | An Intelligent System for Fresh Bitter Gourd Detection Using CNN | |
| dc.type | Other |
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